The ability to automatically identify and inspect objects is important for controlling manufacturing processes, automating processes, and reducing tedious tasks that must be performed by humans. Specialized applications-specific machine vision systems have been historically employed for such systems.
U.S. Pat. No. 4,163,212 to Buerger et al. describes a pattern recognition system which was designed in the late 1970's that used video imagers to recognize the position and orientation of an integrated circuit so as to control a wire bonding machine operation. U.S. Pat. No. 6,748,104 to Bachelder et al. describes a similar system that identifies the position of semiconductor and inspects it based on correlation between images and model patterns (edges, corners or other templates).
U.S. Pat. Nos. 4,696,047 and 4,589,141 to Christian et al. describe systems which were built beginning in the early 1980's that used computer vision-based inspection technology for dedicated inspection applications (U.S. Pat. No. 4,696,047, inspection of electrical connectors and U.S. Pat. No. 4,589,141, inspection of printed labels). U.S. Pat. No. 4,706,120 to Slaughter et al. describes a modular vision system built in the early 1980s that was based on earlier ones built by some of the inventors of the system that is the subject of this patent disclosure. It supported various dedicated inspection applications like those previous described. At this time, modular meant that the system could be included in a larger system as a module.
U.S. Pat. Nos. 5,142,591 and 5,157,486 to Baird et al. describe a system for imaging the silhouette of an ammunition object using a line scan camera and counter to reduce data rate to a microprocessor that implements silhouette boundary inspection of the object as it moves down the conveyer. U.S. Pat. No. 5,311,977 to Dean et al. describes a similar system that singulates objects on a conveyor system and images them using a high-resolution line scan CCD camera. Object images are converted via a camera synchronized counter to a silhouette are compared to reference silhouettes to effect inspection. These disclosures were less focused on the boundary-based inspection algorithm and more on employing specialized pre-processor counter hardware to reduce the computation expense of finding boundary edges in the line scan camera output serial stream.
U.S. Pat. No. 5,608,530 to Gates describes a system for acquiring an object silhouette by employing a laser backlight and measurement of the unobstructed portion of radiation-which has passed the radially opposed halves of the part under measurement. General Inspection, Inc has applied this sensor approach to ammunition inspection and screw inspection. U.S. Pat. No. 5,978,502 to Ohashi describes a system that inspects objects like solder bumps (on a printed circuit card) by comparing range data measured by a sensor to range data representing a good part
U.S. Pat. Nos. 6,040,900 and 6,043,870 to Chen described laser-based imaging system that use sherography and interferometry to form images of precision surface smoothness variations which are related to materials defects in composite materials. U.S. Pat. No. 6,122,001 to Micaletti et al. describes a system that uses laser illumination imaged through a camera system to triangulate the top of packages, which is then used to focus a camera for the purpose of reading package addresses and ultimately automating package sorting.
U.S. Pat. No. 6,448,549 to Safaee-Rad describes a bottle inspection system that determines the quality of threads by capturing a video image, finding the bottleneck, and then assessing thread quality by analyzing the white/dark texture pattern to determine if they resemble bottle threads. U.S. Pat. No. 6,584,805 to Burns et al. describes a inspection machine that extracts simple features from the image of a bottle such as bottle diameter to inspect bottle just after hot molding. U.S. Pat. No. 6,618,495 to Funas describes an inspection machine for back-lit transparent containers that uses a camera to capture an image which is compared by computer to a good container template image (means for defining on said illumination area light intensities varying between the extremes of black and a maximum brightness level on said light source illumination area).
U.S. Pat. No. 6,801,637 to Voronka et al. describes a specialized computer vision system that tracks active light emitters in three line cameras to acquire movement of multiple body positions. The position of each emitter on the body is located through triangulation based on the where the emitter falls along each of the three linear cameras. The system is calibrated by moving a selected light emitter to one or several known position in the movement measurement volume. One calibrated during manufacturing the system retains calibration indefinitely. U.S. Pat. No. 6,831,996 to Williams et al. describes an apparatus that inspects automobile wheels using illumination and a zoom control camera system that acquires wheel reference features (as an example given the milled hole for a valve stem) to determine orientation and then performs inspection by assessing whether the features are in the correct position.
Comparing image derived features to model features expressed at two-dimensional patterns or boundaries has been done in both two and three dimensions for defect detection. However, generally these algorithms have been development specifically for part handling or specific part inspection. U.S. Pat. No. 6,173,066 to Peurach et al. describes a vision processing system that uses a specific approach to pattern recognition of three-dimensional objects or parts from CAD-type templates, matched in multiple views. This system does no initially understand what it is likely to see or in what particular orientation so it describes a staged approach, which hypothesizes object, position and orientation, and follows this up with boundary oriented matching procedure between edges acquired from 3 dimensional images and 3D boundaries defined by the 3D CAD-template. The cameras that take the object images are calibrated through recognition of a calibration object of known shape and size. One calibrated during manufacturing the system retains calibration indefinitely.
U.S. Pat. No. 6,687,398 to Kriwet et al. discloses a method and device for the identification of incorrectly orientated parts and/or parts departing from a predetermined master, the parts being moved by means of a conveyor means past at least one camera for registering the shapes of the parts. U.S. Pat. No. 6,822,181 to Linton describes a part diverter system which might work with a system like Peurach or Kriwet. He describes the use of an actuated paddle to divert an object from an inspection stream (pathway on a conveyor).
U.S. Pat. No. 6,714,671 to Wakitani et al. describes a system that uses model boundary matching to image derived boundaries for inspection of wiring patterns of semiconductors, printed circuit boards, or printed/impressed patterns. U.S. Pat. No. 6,714,679 to Scola et al. describes a boundary analysis technique that determines defects of a boundary to sub-pixel precision and an embodiment for fast correlation scoring for this technique. U.S. Pat. No. 6,856,698 to Silver et al. describes a pattern matching approach that compare model boundary points with edges extracted from imagers.
The prior art demonstrates that:    (1) Computer-vision-based boundary and pattern analysis for inspection has been done since the 1970s.    (2) Prior systems have been specialize to particular inspections to be performed, using special illumination (for instance back lighting and laser illumination), and    (3) Prior systems have, for the most part, been focused on applications where high speed operation is not combined with generality or precision measurement.